EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN)
CERN-EP-2020-230 LHCb-PAPER-2020-037 March 18, 2021Observation
of the B
s
0
→ D
∗±
D
∓
decay
LHCb collaboration† AbstractA search for theB0
s→ D∗±D∓decay is performed using proton-proton collision data
at centre-of-mass energies of 7, 8 and 13 TeV collected by the LHCb experiment,
corresponding to an integrated luminosity of 9 fb−1. The decay is observed with a
high significance and its branching fraction relative to theB0→ D∗±D∓ decay is
measured to be
B(B0
s → D∗±D∓)
B(B0→ D∗±D∓) = 0.137 ± 0.017 ± 0.002 ± 0.006 ,
where the first uncertainty is statistical, the second systematic and the third is due
to the uncertainty on the ratio of the B0
s andB0 hadronisation fractions.
Published in JHEP 03 (2021) 099
© 2021 CERN for the benefit of the LHCb collaboration. CC BY 4.0 licence.
†Authors are listed at the end of this paper.
1
Introduction
The family of B-meson decays into a pair of open-charm mesons are sensitive to elements of the Cabibbo–Kobayashi–Maskawa matrix [1,2]. While B0→ D(∗)+D(∗)−decays can be used
to measure the B0-B0 mixing phase, sin(2β) [3–8], Bs0→ Ds(∗)+Ds(∗)−decays provide access
to the B0
s-B0s mixing phase, φs[9]. Information on additional decays, such as Bs0→ D ∗±D∓,
can be exploited to constrain loop and non-factorisable contributions [10–15], which can be notably prominent [16].
Both B0→ D(∗)+D(∗)−and B0 s→ D
(∗)+
s D(∗)−s decays occur predominantly through tree
or penguin transitions, as shown in Fig. 1. Subleading contributions are expected from W -exchange and penguin-annihilation transitions, illustrated in Fig. 2. In contrast, the Bs0→ D∗±D∓ decay is forbidden at tree level and its dominant contributions originate
from W -exchange and penguin-annihilation diagrams shown in Fig. 2, or from rescattering of intermediate states [17]. Thus, the B0
s→ D
∗±D∓ decay can be used to estimate the
subleading contributions of the B0→ D∗±D∓ decay mode.
The B0
s → D
∗±D∓ decay has not been previously observed, but an excess of
pos-sible B0
s → D
∗±D∓ candidates was seen in a recent measurement of CP violation
in B0→ D∗±D∓ decays by the LHCb experiment [8]. Assuming prominent
contribu-tions from rescattering of e.g. D∗±s D∓s states, the branching fraction is predicted to be (6.1 ± 3.6) × 10−5 [17]. A perturbative QCD approach predicts a much larger branching
fraction of (3.6 ± 0.6) × 10−3 [18].
This paper presents the first observation of the B0 s→ D
∗+D−and B0 s→ D
∗−D+decays,
which have indistinguishable final states. Throughout this paper these decays are treated together and charge conjugation is applied. The branching fraction of the Bs0→ D∗±D∓
decay is measured relative to the B0→ D∗±D∓ decay. Since both decay channels have
the same final state, the experimental systematic uncertainties on the ratio of branching fractions are expected to be small. The measurement uses proton-proton (pp) collision data collected with the LHCb detector in the years 2011, 2012, and 2015–2018 at centre-of-mass energies of 7, 8, and 13 TeV, respectively, corresponding to an integrated luminosity of 9 fb−1. b d, s d, s c c d, s W B0 d,s Dd,s(∗)+ Dd,s(∗)− b d, s d, s c c d, s W g u, c, t B0 d,s D(d,s∗)+ D(d,s∗)−
Figure 1: (Left) Tree-level and (right) penguin diagrams contributing toB0→ D(∗)±
(s) D (∗)∓ and B0 s→ D (∗)± (s) D (∗)∓ s decays.
b c d, s d, s d, s c W B0 d,s Dd,s(∗)− Dd,s(∗)+ b c d, s d, s d, s c W u, c, t Colour Singlet Bd,s0 D(∗)−d,s D(∗)+d,s
Figure 2: (Left) W -exchange and (right) penguin-annihilation diagrams contributing to
B0 (s)→ D (∗)+ (s) D (∗)− (s) decays.
2
Detector and simulation
The LHCb detector [19, 20] is a single-arm forward spectrometer covering the pseudorapidity range 2 < η < 5, designed for the study of particles containing b or c quarks. The detector includes a high-precision tracking system consisting of a silicon-strip vertex detector surrounding the pp interaction region, a large-area silicon-silicon-strip detector located upstream of a dipole magnet with a bending power of about 4 Tm, and three stations of silicon-strip detectors and straw drift tubes placed downstream of the magnet. The tracking system provides a measurement of the momentum, p, of charged particles with a relative uncertainty that varies from 0.5% at low momentum to 1.0% at 200 GeV/c. The minimum distance of a track to a primary pp collision vertex (PV), the impact parameter (IP), is measured with a resolution of (15 + 29/pT) µm, where pT is
the component of the momentum transverse to the beam, in GeV/c. Different types of charged hadrons are distinguished using information from two ring-imaging Cherenkov detectors. Photons, electrons and hadrons are identified by a calorimeter system con-sisting of scintillating-pad and preshower detectors, an electromagnetic and a hadronic calorimeter. Muons are identified by a system composed of alternating layers of iron and multiwire proportional chambers.
Simulated data samples are used to train a multivariate algorithm, model the shapes of mass distributions and calculate efficiencies. In the simulation, pp collisions are generated using Pythia [21] with a specific LHCb configuration [22]. Decays of unstable particles are described by EvtGen [23], in which final-state radiation is generated using Photos [24]. The interaction of the generated particles with the detector, and its response, are implemented using the Geant4 toolkit [25] as described in Ref. [26].
The distributions of particle identification (PID) variables do not match perfectly between simulation and data. This difference is corrected using an approach where functions are constructed that transform the simulated PID response to match calibration samples of recorded data. This is based on a four-dimensional kernel density estimation for distributions in PID value, pT and η of the track and the event multiplicity [27].
3
Candidate selection
Due to varying data-taking conditions, the data samples for the three periods 2011–2012, 2015–2016 and 2017–2018 are treated differently. The online event selection is performed by a trigger, which consists of a hardware stage, based on information from the calorimeter
and muon systems, followed by a software stage, which applies a full event reconstruction. At the hardware trigger stage, events are required to have a muon with high pT or a hadron,
photon or electron with high transverse energy in the calorimeters. The software trigger requires a two-, three- or four-track secondary vertex with a significant displacement from any PV. At least one charged particle must have a large transverse momentum and be inconsistent with originating from any PV. A multivariate algorithm [28, 29] is used for the identification of secondary vertices consistent with the decay of a b hadron.
The B0 (s)→ D
∗±D∓candidates are reconstructed through the decays D∗+→ D0π+with
D0→ K−π+ and D−→ K+π−π−. The tracks of the final-state particles are required to
have a good quality, fulfil loose PID criteria, and have a high χ2
IP value with respect to any
PV, where χ2IP is defined as the difference in the vertex-fit χ2 of a given PV reconstructed with and without the particle being considered. The probability of a candidate being a duplicate track is required to be small. Additionally, the distance of closest approach between all possible combinations of tracks is required to be small. The reconstructed masses of the D∗+, D0 and D− candidates are required to lie inside a mass window of
±50 MeV/c2 around their known values [30], and the difference of the reconstructed masses
between the D∗+ and D0 candidates is required to be smaller than 150 MeV/c2. The ratio of the D− decay time and its uncertainty, t/σt, is required to be larger than −1.
The B0
(s) candidate is reconstructed by combining the D
∗± and D∓ candidates to form a
common vertex. In case multiple PVs are reconstructed in the same event, the PV for which the B0
(s) candidate has the lowest χ 2
IP is assigned as the associated PV. The sum
of the transverse momenta of the decay products of the B0
(s) candidate is required to be
larger than 5 GeV/c and the χ2
IP of the B(s)0 candidate for the associated PV is required
to be small. Furthermore, the decay time of the B0
(s) candidate is required to be larger
than 0.2 ps. Candidates are retained if the mass of the D∗±D∓ system, mD∗±D∓, is in the
range 5000 MeV/c2 < m
D∗±D∓ < 5600 MeV/c2.
Background contributions to D+ candidates arise when kaons or protons stemming
from hadronic decays of Ds+ and Λ+c hadrons are misidentified as pions. A combination of mass and PID requirements is used to suppress contributions from B0→ D∗−
D+s (Λ0 b→ D ∗−Λ+ c) decays with Ds+→ K −K+π+ (Λ+ c → K
−pπ+) to a negligible level. The
mass of the K−π+π+ system from the D+ candidate is recalculated using a kaon (proton) mass hypothesis for either of the pions. The candidate is rejected if the pion has a high probability to be identified as a kaon (proton) and the recomputed mass is compatible with the known D+s (Λ+c) mass [30]. Background contributions that arise from φ(1020) → K−K+ transitions in the D+s decay chain are further suppressed by rejecting candidates if the pion has a high probability to be identified as a kaon and the mass of the K−π+ system, where
the kaon mass is assigned to either of the pions from the D+ decay, is compatible with the known φ(1020) meson mass [30]. Decays of B(s)0 mesons of the form B(s)0 → D∗−h−h+h+ are suppressed by ensuring that the B(s)0 and D+ decay vertices are well separated. Partially reconstructed decays, i.e. decays where one or more final-state particles are not reconstructed, contribute to the lower-mass sideband and are accounted for in the fit to the data.
To suppress combinatorial background from random combinations of final-state tracks, a boosted decision tree (BDT) classifier [31, 32], implemented in the TMVA toolkit [33], utilising the AdaBoost method is used. The BDT classifier is trained using simulated B0 → D∗±D∓ decays as signal proxy and the upper-mass sideband
(5450 MeV/c2 < m
D∗±D∓ < 6000 MeV/c2) as background proxy to avoid contributions from
signal and partially reconstructed decays. For each data-taking period a k-folding tech-nique [34] with k = 5 is adopted. The following variables are used in the training of the BDT classifier: the mass difference of the D∗+ and D0 candidates; PID variables of the
final-state particles of the D− candidate decay, the kaon coming from the D0 candidate
decay and the pion coming from the D∗+ decay; the transverse momenta of the B(s)0 candidate and the kaon from the D− decay; t/σt of the D− candidate; the χ2IP of the B
0 (s)
and D− candidates; the χ2 of the flight distance of the D− and D0 candidates and the χ2
of a kinematic fit to the whole decay chain.
The optimal requirement on the BDT response (also referred to as working point) is determined by maximising the figure-of-merit ε/(a/2 +√NB) [35]. The efficiency of signal
decays, ε, for a specific working point is determined by fits to the data around the known B0 mass [30] before and after the application of the BDT requirement. The number of
background candidates in the B0
s signal region, NB, is estimated with the upper-mass
sideband, and the targeting significance in numbers of the standard deviation, a, is set to three. A three-dimensional scan of the figure-of-merit in the three data-taking periods is conducted, resulting in slightly different working points.
Afterwards, a single candidate is selected randomly from each event containing multiple candidates. The total selection efficiencies of the B0
s→ D
∗±D∓ and B0→ D∗±D∓ decays
are needed for the calculation of the branching fraction ratio and are calculated using simulated samples.
4
Candidate mass fit
To improve the B0
(s) mass resolution, a kinematic fit is applied to the decay chain, where
the masses of the D∗+, D0 and D− candidates are constrained to their known values [30].
An unbinned maximum-likelihood fit to the mass distribution of the D∗±D∓ system is performed separately for each data-taking period to determine the number of signal candidates. To determine the significance of the observation of the B0
s→ D
∗±D∓ decay,
the three likelihoods are added together. The fit model consists of the signal B0→ D∗±D∓
and B0 s→ D
∗±D∓ decays, a contribution from combinatorial background and components
for partially reconstructed B0→ D∗±D∗∓ and B0 s→ D
∗±D∗∓ decays, where one of the D∗
mesons decays into a charged D meson and an unreconstructed π0 meson or photon. The B0→ D∗±D∓ component is modelled by the sum of two Crystal Ball functions [36], with
the same mean but different widths and tail parameters. The B0 s→ D
∗±D∓ component is
described by the same model but with the mean shifted by the difference of the known B0
s and B0 masses [30]. The parameters of the Crystal Ball functions are determined
using fits to simulated B0→ D∗±D∓ decays, apart from their mean and a single scale
factor, which corrects the widths for inaccuracies in simulation. The combinatorial background component is described by an exponential function. The functional forms of the B0→ D∗±D∗∓ and B0
s→ D
∗±D∗∓ contributions depend on the polarisation of the
D∗± mesons and are modelled using simulated decays with a combination of functions corresponding to pure longitudinal and transverse polarisations. For a longitudinally polarised D∗± meson the shape is a double peak, in contrast to a single broad peak for the case of a transversely polarised D∗± system. The free parameters in the fit are the mass of the B0→ D∗±D∓ peak, the scaling factor, the slope of the exponential function,
5000 5200 5400 5600 ] 2 c [MeV/ ± D ± * D m 1 10 2 10 ) 2 Candidates / ( 6 MeV/c 1 − fb 3 LHCb Data Total ± D ± * D → 0 B ± D ± * D → s 0 B ± * D ± * D → 0 B ± * D ± * D → s 0 B Comb. bkg. 5000 5200 5400 5600 ] 2 c [MeV/ ± D ± * D m 1 10 2 10 ) 2 Candidates / ( 6 MeV/c 1 − fb 2 LHCb Data Total ± D ± * D → 0 B ± D ± * D → s 0 B ± * D ± * D → 0 B ± * D ± * D → s 0 B Comb. bkg. 5000 5200 5400 5600 ] 2 c [MeV/ ± D ± * D m 1 10 2 10 3 10 ) 2 Candidates / ( 6 MeV/c 1 − fb 4 LHCb Data Total ± D ± * D → 0 B ± D ± * D → s 0 B ± * D ± * D → 0 B ± * D ± * D → s 0 B Comb. bkg. 5000 5200 5400 5600 ] 2 c [MeV/ ± D ± * D m 0 200 400 600 ) 2 Candidates / ( 6 MeV/c 1 − fb 9 LHCb Data Total ± D ± * D → 0 B ± D ± * D → s 0 B ± * D ± * D → 0 B ± * D ± * D → s 0 B Comb. bkg.
Figure 3: TheD∗±D∓mass distributions for (top left) 2011–2012, (top right) 2015–2016, (bottom
left) 2017–2018 data in logarithmic scale, and (bottom right) the combined data sample in linear scale. The total fit projection is shown as the blue solid line. The green dotted and the red dashed
lines correspond to the signal contributions for the B0 and B0
s decays, respectively. The orange
dash-dotted line corresponds to the combinatorial background contribution. TheB0→ D∗±D∗∓
and B0
s→ D∗±D∗∓ background components are described by the magenta long-dashed and the
cyan long-dashed-two-dotted lines.
the relative fractions between longitudinally and transversely polarised D∗± mesons in B0→ D∗±D∗∓ and B0
s→ D∗±D∗∓decays, and the yields of all shapes. Pseudoexperiments
are used to validate that the model provides unbiased results. The resulting yields of B0→ D∗±D∓ and B0
s→ D
∗±D∓ decays are 466 ± 22 and 12 ± 4
in 2011–2012, 780 ± 29 and 34 ± 7 in 2015–2016, and 1263 ± 36 and 49 ± 8 in 2017–2018, respectively, where the quoted uncertainties are statistical only. The resulting yields are checked by splitting the data in the two final states, D∗+D− and D∗−D+, and are found
to be compatible. The mass distributions and fit projections are shown in Fig. 3 for the three data-taking periods.
5
Systematic uncertainties
The measurement of the ratio of branching fractions, B(B0
s→ D∗±D∓)/B(B0→ D∗±D∓),
relies on input from the measurement of the ratio of the b quark hadronisation fractions to B0
s and B0 mesons, fs/fd. The precision on fs/fd results in the dominant source of
systematic uncertainty. The values are taken from Ref. [37] for 2011–2012 and from Ref. [38] for 2015–2016 and 2017–2018 data-taking periods. Both measurements share sources of systematic uncertainty and thus are treated as partially correlated.
Two sources of systematic uncertainty on the efficiency ratio are considered. The first is caused by the finite size of the simulated data samples. The second originates in the use of PID variables, whose distributions do not match perfectly between data and simulation. This uncertainty is determined by choosing a different kernel density estimation in the transformation of the simulated PID response and calculating the difference of the resulting efficiency ratio.
The systematic uncertainties on the signal yields due to the fit models are evaluated using pseudoexperiments. A systematic uncertainty due to the choice of the signal model and the assumption that the B0 and B0
s distributions have identical shape in the mass fit
is evaluated. Candidates are generated with a mass distribution described by a Hypatia function [39] with different parameters for the B0 and Bs0 models. The values of the parameters of the Hypatia function are determined by a fit to simulated B0→ D∗±D∓
and B0
s→ D
∗±D∓ data, respectively. All yields and background parameters are set to
the values found in the default fit to the data. The generated candidates are then fitted with the default model and the result for the branching fraction ratio is calculated for each fit. The mean of all experiments and its residual are calculated for the three periods separately. The residual and its uncertainty are summed in quadrature and the square root is assigned as the systematic uncertainty.
In addition, a systematic uncertainty due to the model of the combinatorial background is evaluated. Pseudoexperiments are used with parameters of the signal and partially reconstructed background models set to the values found in the default result. The slope of the exponential function is extracted by a fit to the data, where a looser BDT requirement is applied to enhance the contribution of the combinatorial background. The generated candidates are fitted with the default model and the systematic uncertainty is calculated in the same way as the systematic uncertainty for the signal model.
To determine the systematic uncertainty on the combined result, the systematic uncertainties related to the PID variables, signal model and background model are assumed to be fully correlated between the data-taking periods when calculating the weighted average. The systematic uncertainties due to the finite size of the simulated data are assumed to be uncorrelated. All systematic uncertainties are added in quadrature to obtain the total systematic uncertainty per data-taking period, and are listed together with their contributions in Table 1.
6
Results
The B0
s→ D
∗±D∓ decay is observed with a high significance, which is calculated using
Table 1: Systematic uncertainties on B(B0
s → D∗±D∓)/B(B0 → D∗±D∓). The systematic
uncertainty is given relative to the measured value.
Source 2011–2012 [%] 2015–2016 [%] 2017–2018 [%] Combined [%]
fs/fd [37, 38] 5.8 4.9 4.9 4.6
Simulated data size 0.8 1.2 0.8 0.6
PID 0.7 0.7 0.8 0.7
Signal model 0.1 0.1 0.5 0.3
Background model 1.7 1.3 0.8 1.1
Total without fs/fd 2.0 1.9 1.5 1.5
Total 6.1 5.3 5.1 4.8
ratio is calculated using the expression B(B0 s→ D ∗±D∓) B(B0→ D∗±D∓) = NB0 s NB0 εB0 εB0 s fd fs , where the B0 s and B0 yields, NB0
s and NB0, are determined from the fit to the D
∗±D∓
mass distribution. The ratios of the B0s→ D∗±D∓ and B0→ D∗±D∓ selection efficiencies,
εB0
s/εB0, calculated using simulation samples for the data-taking periods 2011–2012,
2015–2016 and 2017–2018 are 1.063 ± 0.008, 1.062 ± 0.013 and 1.074 ± 0.009, respectively, where the uncertainties are statistical. The ratios of the hadronisation fractions are taken as 0.259 ± 0.015 and 0.244 ± 0.012 for the 2011–2012 [37] and 2015–2018 [38] data-taking periods, respectively.
The ratios of branching fractions are found to be B(B0 s→ D ∗±D∓) B(B0→ D∗±D∓) 2011–2012 = 0.093 ± 0.032 ± 0.002 ± 0.005 , B(B0 s→ D ∗±D∓) B(B0→ D∗±D∓) 2015–2016 = 0.168 ± 0.034 ± 0.003 ± 0.008 , B(B0 s→ D ∗±D∓) B(B0→ D∗±D∓) 2017–2018 = 0.149 ± 0.024 ± 0.002 ± 0.007 ,
where the first uncertainty is statistical, the second systematic and the third is due to the uncertainty of the fragmentation fraction ratio fs/fd. Using the quadratic sums of the
uncertainties as weights and including the correlation of the systematic uncertainties, the average of these measurements is
B(B0
s → D
∗±D∓)
B(B0 → D∗±D∓) = 0.137 ± 0.017 ± 0.002 ± 0.006 .
Using the measured value of the B0→ D∗±D∓ branching fraction from Ref. [6], the
B0 s→ D
∗±D∓ branching fraction is determined to be
B(B0
s → D
∗±
D∓) = (8.41 ± 1.02 ± 0.12 ± 0.39 ± 0.79) × 10−5,
This result assumes an average B0
s lifetime for the Bs0→ D
∗±D∓ decay. The heavy and
light eigenstates of the B0
s meson have significantly different lifetimes. As the selection
efficiency depends on the lifetime, correction factors for the efficiency are calculated following the procedure outlined in Ref. [42] that considers either a purely heavy or a purely light B0
s eigenstate. The correction factors are found to be compatible for all
data-taking periods. The integrated correction factors are 1.042 (0.949) for a purely heavy (light) B0
s eigenstate. The equivalent effect in the selection efficiency of the B0 decay is
negligible due to the small value of ∆Γd [30].
7
Conclusion
This paper presents the first observation of the B0
s → D
∗±D∓ decay along with the
measurement of its branching fraction relative to the B0→ D∗±D∓ decay. The analysis
is performed with data collected by the LHCb experiment in the years 2011, 2012, and 2015 to 2018, corresponding to an integrated luminosity of 9 fb−1. The combined ratio of branching fractions for all data-taking periods is determined to be
B(B0
s → D∗±D∓)
B(B0 → D∗±D∓) = 0.137 ± 0.017 ± 0.002 ± 0.006 ,
where the first uncertainty is statistical, the second systematic and the third is due to the uncertainty of the fragmentation fraction ratio fs/fd. The Bs0→ D∗±D∓ branching
fraction is determined to be
B(B0
s → D
∗±
D∓) = (8.41 ± 1.02 ± 0.12 ± 0.39 ± 0.79) × 10−5,
where the fourth uncertainty is due to the B0→ D∗±D∓ branching fraction [30]. The
result is in agreement with predictions from other B-meson decays [17] and disagrees with predictions from a perturbative QCD approach [18]. It can be used to constrain subleading contributions in the measurement of the CP -violating parameter sin(2β) with B0→ D∗±D∓ decays.
Acknowledgements
We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from CERN and from the national agencies: CAPES, CNPq, FAPERJ and FINEP (Brazil); MOST and NSFC (China); CNRS/IN2P3 (France); BMBF, DFG and MPG (Germany); INFN (Italy); NWO (Netherlands); MNiSW and NCN (Poland); MEN/IFA (Romania); MSHE (Russia); MICINN (Spain); SNSF and SER (Switzerland); NASU (Ukraine); STFC (United Kingdom); DOE NP and NSF (USA). We acknowledge the computing resources that are provided by CERN, IN2P3 (France), KIT and DESY (Germany), INFN (Italy), SURF (Netherlands), PIC (Spain), GridPP (United Kingdom), RRCKI and Yandex LLC (Russia), CSCS (Switzerland), IFIN-HH (Romania), CBPF (Brazil), PL-GRID (Poland) and OSC (USA). We are indebted to the communities behind the multiple open-source software packages on which we depend. Individual groups or members have received support from AvH Foundation (Germany); EPLANET, Marie
Sk lodowska-Curie Actions and ERC (European Union); A*MIDEX, ANR, Labex P2IO and OCEVU, and R´egion Auvergne-Rhˆone-Alpes (France); Key Research Program of Frontier Sciences of CAS, CAS PIFI, CAS CCEPP, Fundamental Research Funds for Central Universities, and Sci. & Tech. Program of Guangzhou (China); RFBR, RSF and Yandex LLC (Russia); GVA, XuntaGal and GENCAT (Spain); the Royal Society and the Leverhulme Trust (United Kingdom).
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C.A. Aidala84, S. Aiola25, Z. Ajaltouni9, S. Akar64, J. Albrecht14, F. Alessio47, M. Alexander58,
A. Alfonso Albero44, Z. Aliouche61, G. Alkhazov37, P. Alvarez Cartelle47, S. Amato2,
Y. Amhis11, L. An21, L. Anderlini21, A. Andreianov37, M. Andreotti20, F. Archilli16,
A. Artamonov43, M. Artuso67, K. Arzymatov41, E. Aslanides10, M. Atzeni49, B. Audurier11,
S. Bachmann16, M. Bachmayer48, J.J. Back55, S. Baker60, P. Baladron Rodriguez45,
V. Balagura11, W. Baldini20,47, J. Baptista Leite1, R.J. Barlow61, S. Barsuk11, W. Barter60,
M. Bartolini23,i, F. Baryshnikov80, J.M. Basels13, G. Bassi28, B. Batsukh67, A. Battig14,
A. Bay48, M. Becker14, F. Bedeschi28, I. Bediaga1, A. Beiter67, V. Belavin41, S. Belin26,
V. Bellee48, K. Belous43, I. Belov39, I. Belyaev38, G. Bencivenni22, E. Ben-Haim12,
A. Berezhnoy39, R. Bernet49, D. Berninghoff16, H.C. Bernstein67, C. Bertella47, E. Bertholet12,
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J. Bhom33, L. Bian72, M.S. Bieker14, S. Bifani52, P. Billoir12, M. Birch60, F.C.R. Bishop54,
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G. Wilkinson62, M. Wilkinson67, I. Williams54, M. Williams63,68, M.R.J. Williams57,
F.F. Wilson56, W. Wislicki35, M. Witek33, L. Witola16, G. Wormser11, S.A. Wotton54, H. Wu67,
K. Wyllie47, Z. Xiang5, D. Xiao7, Y. Xie7, A. Xu4, J. Xu5, L. Xu3, M. Xu7, Q. Xu5, Z. Xu5,
Z. Xu4, D. Yang3, Y. Yang5, Z. Yang3, Z. Yang65, Y. Yao67, L.E. Yeomans59, H. Yin7, J. Yu70,
X. Yuan67, O. Yushchenko43, E. Zaffaroni48, K.A. Zarebski52, M. Zavertyaev15,c, M. Zdybal33,
O. Zenaiev47, M. Zeng3, D. Zhang7, L. Zhang3, S. Zhang4, Y. Zhang4, Y. Zhang62,
A. Zhelezov16, Y. Zheng5, X. Zhou5, Y. Zhou5, X. Zhu3, V. Zhukov13,39, J.B. Zonneveld57,
S. Zucchelli19,e, D. Zuliani27, G. Zunica61.
1Centro Brasileiro de Pesquisas F´ısicas (CBPF), Rio de Janeiro, Brazil
2Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, Brazil
3Center for High Energy Physics, Tsinghua University, Beijing, China
4School of Physics State Key Laboratory of Nuclear Physics and Technology, Peking University, Beijing,
China
5University of Chinese Academy of Sciences, Beijing, China
6Institute Of High Energy Physics (IHEP), Beijing, China
7Institute of Particle Physics, Central China Normal University, Wuhan, Hubei, China
8Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, IN2P3-LAPP, Annecy, France
9Universit´e Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France
10Aix Marseille Univ, CNRS/IN2P3, CPPM, Marseille, France
11Universit´e Paris-Saclay, CNRS/IN2P3, IJCLab, Orsay, France
12LPNHE, Sorbonne Universit´e, Paris Diderot Sorbonne Paris Cit´e, CNRS/IN2P3, Paris, France
13I. Physikalisches Institut, RWTH Aachen University, Aachen, Germany
14Fakult¨at Physik, Technische Universit¨at Dortmund, Dortmund, Germany
15Max-Planck-Institut f¨ur Kernphysik (MPIK), Heidelberg, Germany
16Physikalisches Institut, Ruprecht-Karls-Universit¨at Heidelberg, Heidelberg, Germany
17School of Physics, University College Dublin, Dublin, Ireland
18INFN Sezione di Bari, Bari, Italy
19INFN Sezione di Bologna, Bologna, Italy
20INFN Sezione di Ferrara, Ferrara, Italy
21INFN Sezione di Firenze, Firenze, Italy
22INFN Laboratori Nazionali di Frascati, Frascati, Italy
23INFN Sezione di Genova, Genova, Italy
24INFN Sezione di Milano-Bicocca, Milano, Italy
25INFN Sezione di Milano, Milano, Italy
26INFN Sezione di Cagliari, Monserrato, Italy
27Universita degli Studi di Padova, Universita e INFN, Padova, Padova, Italy
28INFN Sezione di Pisa, Pisa, Italy
29INFN Sezione di Roma Tor Vergata, Roma, Italy
30INFN Sezione di Roma La Sapienza, Roma, Italy
31Nikhef National Institute for Subatomic Physics, Amsterdam, Netherlands
32Nikhef National Institute for Subatomic Physics and VU University Amsterdam, Amsterdam,
Netherlands
33Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences, Krak´ow, Poland
34AGH - University of Science and Technology, Faculty of Physics and Applied Computer Science,
Krak´ow, Poland
35National Center for Nuclear Research (NCBJ), Warsaw, Poland
37Petersburg Nuclear Physics Institute NRC Kurchatov Institute (PNPI NRC KI), Gatchina, Russia
38Institute of Theoretical and Experimental Physics NRC Kurchatov Institute (ITEP NRC KI), Moscow,
Russia
39Institute of Nuclear Physics, Moscow State University (SINP MSU), Moscow, Russia
40Institute for Nuclear Research of the Russian Academy of Sciences (INR RAS), Moscow, Russia
41Yandex School of Data Analysis, Moscow, Russia
42Budker Institute of Nuclear Physics (SB RAS), Novosibirsk, Russia
43Institute for High Energy Physics NRC Kurchatov Institute (IHEP NRC KI), Protvino, Russia,
Protvino, Russia
44ICCUB, Universitat de Barcelona, Barcelona, Spain
45Instituto Galego de F´ısica de Altas Enerx´ıas (IGFAE), Universidade de Santiago de Compostela,
Santiago de Compostela, Spain
46Instituto de Fisica Corpuscular, Centro Mixto Universidad de Valencia - CSIC, Valencia, Spain
47European Organization for Nuclear Research (CERN), Geneva, Switzerland
48Institute of Physics, Ecole Polytechnique F´ed´erale de Lausanne (EPFL), Lausanne, Switzerland
49Physik-Institut, Universit¨at Z¨urich, Z¨urich, Switzerland
50NSC Kharkiv Institute of Physics and Technology (NSC KIPT), Kharkiv, Ukraine
51Institute for Nuclear Research of the National Academy of Sciences (KINR), Kyiv, Ukraine
52University of Birmingham, Birmingham, United Kingdom
53H.H. Wills Physics Laboratory, University of Bristol, Bristol, United Kingdom
54Cavendish Laboratory, University of Cambridge, Cambridge, United Kingdom
55Department of Physics, University of Warwick, Coventry, United Kingdom
56STFC Rutherford Appleton Laboratory, Didcot, United Kingdom
57School of Physics and Astronomy, University of Edinburgh, Edinburgh, United Kingdom
58School of Physics and Astronomy, University of Glasgow, Glasgow, United Kingdom
59Oliver Lodge Laboratory, University of Liverpool, Liverpool, United Kingdom
60Imperial College London, London, United Kingdom
61Department of Physics and Astronomy, University of Manchester, Manchester, United Kingdom
62Department of Physics, University of Oxford, Oxford, United Kingdom
63Massachusetts Institute of Technology, Cambridge, MA, United States
64University of Cincinnati, Cincinnati, OH, United States
65University of Maryland, College Park, MD, United States
66Los Alamos National Laboratory (LANL), Los Alamos, United States
67Syracuse University, Syracuse, NY, United States
68School of Physics and Astronomy, Monash University, Melbourne, Australia, associated to55
69Pontif´ıcia Universidade Cat´olica do Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil, associated to 2
70Physics and Micro Electronic College, Hunan University, Changsha City, China, associated to 7
71Guangdong Provencial Key Laboratory of Nuclear Science, Institute of Quantum Matter, South China
Normal University, Guangzhou, China, associated to 3
72School of Physics and Technology, Wuhan University, Wuhan, China, associated to 3
73Departamento de Fisica , Universidad Nacional de Colombia, Bogota, Colombia, associated to 12
74Universit¨at Bonn - Helmholtz-Institut f¨ur Strahlen und Kernphysik, Bonn, Germany, associated to 16
75Institut f¨ur Physik, Universit¨at Rostock, Rostock, Germany, associated to 16
76INFN Sezione di Perugia, Perugia, Italy, associated to 20
77Van Swinderen Institute, University of Groningen, Groningen, Netherlands, associated to 31
78Universiteit Maastricht, Maastricht, Netherlands, associated to 31
79National Research Centre Kurchatov Institute, Moscow, Russia, associated to 38
80National University of Science and Technology “MISIS”, Moscow, Russia, associated to38
81National Research University Higher School of Economics, Moscow, Russia, associated to41
82National Research Tomsk Polytechnic University, Tomsk, Russia, associated to38
83DS4DS, La Salle, Universitat Ramon Llull, Barcelona, Spain, associated to44
84University of Michigan, Ann Arbor, United States, associated to67
aUniversidade Federal do Triˆangulo Mineiro (UFTM), Uberaba-MG, Brazil
bLaboratoire Leprince-Ringuet, Palaiseau, France
cP.N. Lebedev Physical Institute, Russian Academy of Science (LPI RAS), Moscow, Russia
eUniversit`a di Bologna, Bologna, Italy
fUniversit`a di Cagliari, Cagliari, Italy
gUniversit`a di Ferrara, Ferrara, Italy
hUniversit`a di Firenze, Firenze, Italy
iUniversit`a di Genova, Genova, Italy
jUniversit`a di Milano Bicocca, Milano, Italy
kUniversit`a di Roma Tor Vergata, Roma, Italy
lAGH - University of Science and Technology, Faculty of Computer Science, Electronics and
Telecommunications, Krak´ow, Poland
mUniversit`a di Padova, Padova, Italy
nUniversit`a di Pisa, Pisa, Italy
oUniversit`a degli Studi di Milano, Milano, Italy
pUniversit`a di Urbino, Urbino, Italy
qUniversit`a della Basilicata, Potenza, Italy
rScuola Normale Superiore, Pisa, Italy
sUniversit`a di Modena e Reggio Emilia, Modena, Italy
tUniversit`a di Siena, Siena, Italy
uMSU - Iligan Institute of Technology (MSU-IIT), Iligan, Philippines
